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In settings where frequent data collection is limited, developing predictive models provides a cost-effective means of anticipating risks to enhance rapid life-saving action, supporting timely evidence-based interventions. This study develops predictive models, applying Machine Learning (ML) approaches, capable of accurately forecasting household-level food insecurity. This study used data from the Ethiopia-High Frequency Phone Survey, collected by World Bank. The performances of Machine Learning models and classical logistic regression model, in predicting households\u2019 food insecurity, were compared. Predictive models were trained and validated (internally and temporally) to evaluate model generalizability over time. ML models significantly outperformed the traditional logistic regression model in predicting household food insecurity. Based on the Brier score, the ML models demonstrated higher predictive accuracy and better calibration than the classical model. Regularization through optimized hyperparameters improved model stability and feature selection. While ridge, lasso, and elastic-net regressions produced similar coefficient directions, they differed in the number of selected predictors\u2014the lasso model identified minimum key variables with comparable predictive accuracy. Temporal validation confirmed that the ML models maintained strong predictive performance, demonstrating their reusability and generalizability across time. The lasso regression model demonstrated strong predictive capability by selecting a manageable set of relevant features, resulting in reduced model complexity and improved interpretability. Integrating such ML models into food security monitoring systems can help policymakers design timely and data-driven interventions, enabling proactive responses in resource-constrained environment.<\/jats:p>","DOI":"10.1038\/s41598-026-53425-3","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T06:55:48Z","timestamp":1779346548000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-enhanced modeling approach for optimally predicting household level food insecurity in Ethiopia during COVID-19"],"prefix":"10.1038","volume":"16","author":[{"given":"Henok Wariso","family":"Waqo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gezahegn Mekonnen","family":"Woldemedihn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yehenew Getachew","family":"Kifle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Konietschke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeytu Gashaw","family":"Asfaw","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,21]]},"reference":[{"key":"53425_CR1","doi-asserted-by":"publisher","unstructured":"FAO. 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Prevalence and associated factors for rural households food insecurity in selected districts of East Gojjam Zone, Northern Ethiopia: Cross-sectional study. BMC Public Health 20(1), 1\u201314. https:\/\/doi.org\/10.1186\/s12889-020-8220-0 (2020).","journal-title":"BMC Public Health"},{"issue":"5","key":"53425_CR7","doi-asserted-by":"publisher","first-page":"64","DOI":"10.11648\/j.ijtam.20200605.12","volume":"6","author":"E Belachew Sime","year":"2020","unstructured":"Belachew Sime, E. Determinants of household food insecurity in rural Ethiopia: Multiple linear regression (classical and bayesian approaches). Int. J. Theor. Appl. Math. 6(5), 64. https:\/\/doi.org\/10.11648\/j.ijtam.20200605.12 (2020).","journal-title":"Int. J. Theor. Appl. Math."},{"issue":"1","key":"53425_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-23918-y","volume":"12","author":"ZG Dessie","year":"2022","unstructured":"Dessie, Z. G., Zewotir, T. & North, D. 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